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197 results for “time estimation”
Data from: The past sure is tense: on interpreting phylogenetic divergence time estimates
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Data from: Population genetic and field ecological analyses return similar estimates of dispersal over space and time in an endangered amphibian
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Data from: Hide and seek in vegetation: time-to-detection is an efficient design for estimating detectability and occurrence
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Data from: Repeatable, continuous and real-time estimates of coupled nitrogenase activity and carbon exchange at the whole-plant scale
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Estimating the time since admixture from phased and unphased molecular data
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Data for manuscript "Estimate of airborne transmission of SARS-CoV-2 using real time tracking of healthcare workers."
<p>These are the data sets for a manuscript to calculate the transmissability of SARS CoV-2 via the airborne route. These data have the duration of exposure of healthcare workers to SARS CoV-2 and the code used to analyze to estimate q, and the comparison to other pathogens.</p>
Divergence time estimation of genus Tribolium by extensive sampling of highly conserved orthologs
<p><i>Tribolium castaneum</i>, the red flour beetle, is among the most well-studied eukaryotic genetic model organisms. <i>Tribolium</i> often serves as a comparative bridge from highly derived <i>Drosophila</i> traits to other organisms. Simultaneously, as a member of the most diverse order of metazoans, Coleoptera, <i>Tribolium</i> informs us about innovations that accompany hyper diversity. However, understanding the tempo and mode of evolutionary innovation requires well-resolved, time-calibrated phylogenies, which are not available for <i>Tribolium</i>. The most recent effort to understand <i>Tribolium</i>phylogenetics used two mitochondrial and three nuclear markers. The study concluded that the genus may be paraphyletic and reported a broad range for divergence time estimates. Here we employ recent advances in Bayesian methods to estimate the relationships and divergence times among <i>Tribolium castaneum</i>, <i>T. brevicornis</i>, <i>T. confusum</i>, <i>T. freemani</i>, and <i>Gnatocerus cornutus </i>using 1368 orthologs conserved across all five species and an independent substitution rate estimate. We find that the most basal split within <i>Tribolium</i> occurred ~86 Mya [95% HPD 85.90–87.04 Mya] and that the most recent split was between <i>T. freemani</i> and <i>T. castaneum</i> at ~14 Mya [95% HPD 13.55-14.00]. Our results are consistent with broader phylogenetic analyses of insects and suggest that Cenozoic climate changes played a role in the <i>Tribolium </i>diversification.</p>
Data from: Robustness to divergence time underestimation when inferring species trees from estimated gene trees
To infer species trees from gene trees estimated from phylogenomic data sets, tractable methods are needed that can handle dozens to hundreds of loci. We examine several computationally efficient approaches—MP-EST, STAR, STEAC, STELLS, and STEM—for inferring species trees from gene trees estimated using maximum likelihood (ML) and Bayesian approaches. Among the methods examined, we found that topology-based methods often performed better using ML gene trees and methods employing coalescent times typically performed better using Bayesian gene trees, with MP-EST, STAR, STEAC, and STELLS outperforming STEM under most conditions. We examine why the STEM tree (also called GLASS or Maximum Tree) is less accurate on estimated gene trees by comparing estimated and true coalescence times, performing species tree inference using simulations, and analyzing a great ape data set keeping track of false positive and false negative rates for inferred clades. We find that although true coalescence times are more ancient than speciation times under the multispecies coalescent model, estimated coalescence times are often more recent than speciation times. This underestimation can lead to increased bias and lack of resolution with increased sampling (either alleles or loci) when gene trees are estimated with ML. The problem appears to be less severe using Bayesian gene-tree estimates.
Data from: Bayesian estimation of species divergence times using correlated quantitative characters
Discrete morphological data have been widely used to study species evolution, but the use of quantitative (or continuous) morphological characters is less common. Here, we implement a Bayesian method to estimate species divergence times using quantitative characters. Quantitative character evolution is modelled using Brownian diffusion with character correlation and character variation within populations. Through simulations, we demonstrate that ignoring the population variation (or population "noise") and the correlation among characters leads to biased estimates of divergence times and rate, especially if the correlation and population noise are high. We apply our new method to the analysis of quantitative characters (cranium landmarks) and molecular data from carnivoran mammals. Our results show that time estimates are affected by whether the correlations and population noise are accounted for or ignored in the analysis. The estimates are also affected by the type of data analysed, with analyses of morphological characters only, molecular data only, or a combination of both; showing noticeable differences among the time estimates. Rate variation of morphological characters among the carnivoran species appears to be very high, with Bayesian model selection indicating that the independent-rates model fits the morphological data better than the autocorrelated-rates model. We suggest that using morphological continuous characters, together with molecular data, can bring a new perspective to the study of species evolution. Our new model is implemented in the MCMCtree computer program for Bayesian inference of divergence times.
Data from: Using phylogenomic data to explore the effects of relaxed clocks and calibration strategies on divergence time estimation: primates as a test case
Primates have long been a test case for the development of phylogenetic methods for divergence time estimation. Despite a large number of studies, however, the timing of origination of crown Primates relative to the K-Pg boundary and the timing of diversification of the main crown groups remain controversial. Here we analysed a dataset of 372 taxa (367 Primates and 5 outgroups, 3.4 million aligned base pairs) that includes nine primate genomes. We systematically explore the effect of different interpretations of fossil calibrations and molecular clock models on primate divergence time estimates. We find that even small differences in the construction of fossil calibrations can have a noticeable impact on estimated divergence times, especially for the oldest nodes in the tree. Notably, choice of molecular rate model (auto-correlated or independently distributed rates) has an especially strong effect on estimated times, with the independent rates model producing considerably more ancient age estimates for the deeper nodes in the phylogeny. We implement thermodynamic integration, combined with Gaussian quadrature, in the program MCMCTree, and use it to calculate Bayes factors for clock models. Bayesian model selection indicates that the auto-correlated rates model fits the primate data substantially better, and we conclude that time estimates under this model should be preferred. We show that for eight core nodes in the phylogeny, uncertainty in time estimates is close to the theoretical limit imposed by fossil uncertainties. Thus, these estimates are unlikely to be improved by collecting additional molecular sequence data. All analyses place the origin of Primates close to the K-Pg boundary, either in the Cretaceous or straddling the boundary into the Palaeogene.
Estimating uncertainty in divergence times among three-spined stickleback clades using the multispecies coalescent
<p>Incomplete lineage sorting (ILS) can lead to biased divergence time estimates. To explore if and how ILS has influenced the results of a recent study of worldwide phylogeny of three-spined sticklebacks (Gasterosteus aculeatus), we estimated divergence times among major clades by applying both a concatenation approach and the multispecies coalescent (MSC) model to single-nucleotide polymorphisms. To further test the influence of different calibration strategies, we applied different calibrations to the root and to younger nodes in addition to the ones used in the original study. Both the updated calibrations and the application of the MSC model influenced divergence time estimates, sometimes significantly. The new divergence time estimates were more ancient than in the previous study for older nodes, whereas the estimates of younger nodes were not strongly affected by the re-analyses. However, given the applied MSC method employs a simple substitution model and cannot account for changes in population size, we suggest that different analytical approaches and calibration strategies should be used in order to explore uncertainty in divergence time estimates. This study provides a valuable reference timeline for the ages of worldwide three-spined stickleback populations and emphasizes the need to embrace, rather than obscure, uncertainties around divergence time estimates.</p>
Data from: A well-constrained estimate for the timing of the salmonid whole genome duplication reveals major decoupling from species diversification
Whole genome duplication (WGD) is often considered to be mechanistically associated with species diversification. Such ideas have been anecdotally attached to a WGD at the stem of the salmonid fish family, but remain untested. Here, we characterized an extensive set of gene paralogues retained from the salmonid WGD, in species covering the major lineages (subfamilies Salmoninae, Thymallinae and Coregoninae). By combining the data in calibrated relaxed molecular clock analyses, we provide the first well-constrained and direct estimate for the timing of the salmonid WGD. Our results suggest that the event occurred no later in time than 88 Ma and that 40–50 Myr passed subsequently until the subfamilies diverged. We also recovered a Thymallinae–Coregoninae sister relationship with maximal support. Comparative phylogenetic tests demonstrated that salmonid diversification patterns are closely allied in time with the continuous climatic cooling that followed the Eocene–Oligocene transition, with the highest diversification rates coinciding with recent ice ages. Further tests revealed considerably higher speciation rates in lineages that evolved anadromy—the physiological capacity to migrate between fresh and seawater—than in sister groups that retained the ancestral state of freshwater residency. Anadromy, which probably evolved in response to climatic cooling, is an established catalyst of genetic isolation, particularly during environmental perturbations (for example, glaciation cycles). We thus conclude that climate-linked ecophysiological factors, rather than WGD, were the primary drivers of salmonid diversification.
Data from: Interpreting the estimated timing of migration events between hybridizing species
The question of whether speciation can occur in the presence of gene flow has long been a contentious one. However, measuring the amount and timing of gene flow remains challenging. The computer program IMA2 allows researchers to estimate the timing of migration events for each locus during analyses, and these estimates have been used to infer the timing of introgression and mode of speciation. We use simulated datasets to examine the degree to which gene flow timing estimates can be used for these purposes, and what demographic conditions and datasets may be most amenable to gene flow timing estimation. We find that the 90% highest posterior density (HPD) interval of gene flow timing is almost always substantially wider than the actual window of gene flow, and increasing the information content of the dataset in terms of number of loci, number of sequences sampled, or locus length (and thus number of variable sites) has little impact on the posterior distribution over the range of values we tested. Even when simulated gene flow only occurred over the most recent 0.01% of the species' history, the HPD interval usually encompasses the inferred divergence time. Our results indicate that gene flow timing estimates made using the method currently implemented in IMA2 cannot reliably be used to make inferences about the timing of introgression between diverged species or to distinguish between speciation with gene flow and allopatric speciation followed by one or more episodes of gene flow.
FIGURE 2. Penaeini ultrametric tree. Below a in Molecular phylogeny and divergence time estimates of Penaeid Shrimp Lineages (Decapoda: Penaeidae)
FIGURE 2. Penaeini ultrametric tree. Below a geological time-scale is shown.
Data from: A time series model for estimating temporal variation in phenotypic selection on laying dates in a Dutch great tit population
[No abstract entered]
Grape V1 Data: Frequency Estimation and Amplitudes of North American Time Standard Stations
<p>Frequency and amplitude estimation data collected by Grape V1 stations in the Personal Space Weather Station network. <br><br>As of the 2 March 2023 upload, data is separated into folders by year. (One zipped folder is too big to upload to Zenodo without error.)</p> <p>Data in 2024 onward is separated into folders by month.</p>
Table 2 in Using photo by-catch data to reliably estimate spotted hyaena densities over time
<p><b>Table 2:</b> Summary of spotted hyaena density estimates for the Hluhluwe and iMfolozi sections in the Hluhluwe-iMfolozi Park, KwaZulu-Natal, South Africa.</p><table><tbody><tr><th><b>Year</b></th><th><b>Hluhluwe density</b></th><th><b>Hluluwe LCL</b></th><th><b>Hluhluwe UCL</b></th><th><b>iMfolozi density</b></th><th><b>iMfolozi LCL</b></th><th><b>iMfolozi UCL</b></th></tr></tbody><tbody><tr><th>2013</th><td>17.59</td><td>11.74</td><td>26.36</td><td>21.41</td><td>15.38</td><td>29.82</td></tr><tr><th>2014</th><td>14.39</td><td>9.32</td><td>22.21</td><td>27.09</td><td>20.9</td><td>35.11</td></tr><tr><th>2015</th><td>9.78</td><td>4.41</td><td>21.69</td><td>12.98</td><td>9.18</td><td>18.37</td></tr><tr><th>2016</th><td>8.54</td><td>4.69</td><td>15.55</td><td>24.31</td><td>17.j26</td><td>34.23</td></tr><tr><th>2017</th><td>12.9</td><td>8.28</td><td>20.11</td><td>21.34</td><td>15.25</td><td>29.87</td></tr><tr><th>2018</th><td>14.6</td><td>9.88</td><td>21.58</td><td>27.27</td><td>20.34</td><td>36.56</td></tr><tr><th>Mean</th><td>12.97</td><td>8.05</td><td>21.25</td><td>22.40</td><td>16.21</td><td>30.66</td></tr><tr><th>Stdev</th><td>3.34</td><td>2.94</td><td>3.50</td><td>5.30</td><td>4.75</td><td>6.63</td></tr></tbody></table><p>The SECR analysis used a uniform 12,500 m buffer effective trapping area.LCL,lower confidence limit;UCL,upper confidence limit.Estimates are measured in hyaena per 100 km <sup>2</sup>.</p>
Table 1 in Using photo by-catch data to reliably estimate spotted hyaena densities over time
<p><b>Table 1:</b> Summary of camera trapping parameters and density estimates for spotted hyaenas in the Hluhluwe-iMfolozi Park, KwaZulu-Natal, South Africa.</p><table><tbody><tr><th></th><th></th><th><b>2013</b></th><th><b>2014</b></th><th><b>2015</b></th><th><b>2016 2017</b></th><th><b>2018</b></th><th><b>Total</b></th></tr></tbody><tbody><tr><th>Survey details</th><td>View</td><td>RHS</td><td>RHS</td><td>RHS</td><td>LHS</td><td>RHS</td><td></td><td>RHS</td><td></td></tr><tr><th></th><td>Individuals</td><td>79</td><td>97</td><td>83</td><td>59</td><td>77</td><td></td><td>98</td><td>230</td></tr><tr><th></th><td>Juvenile captures</td><td>2</td><td>2</td><td>1</td><td>0</td><td>10</td><td></td><td>19</td><td></td></tr><tr><th></th><td>Independent capture events</td><td>159</td><td>281</td><td>263</td><td>113</td><td>173</td><td></td><td>294</td><td>1242</td></tr><tr><th></th><td>MMDM (m)</td><td>3047</td><td>3944</td><td>5453</td><td>2559</td><td>3424</td><td></td><td>3744</td><td></td></tr><tr><th></th><td>Trap nights</td><td>1342</td><td>1986</td><td>2039</td><td>2031</td><td>1917</td><td></td><td>1929</td><td>11,244</td></tr><tr><th></th><td>Population closure test <i>p-</i> value</td><td>>0.05</td><td>>0.05</td><td><0.05>0.05 <0.05</td><td>>0.05</td><td></td></tr><tr><th></th><td></td><td><b>2013</b></td><td><b>2014</b></td><td><b>2015</b></td><td><b>2016</b></td><td><b>2017</b></td><td><b>2018</b></td><td></td><td><b>Mean</b></td></tr><tr><th>Model results</th><td>Density per 100 km2</td><td>20.13</td><td>20.83</td><td>11.98</td><td>17.77</td><td>19.00</td><td>20.04</td><td></td><td>18.29 ± 3.27</td></tr><tr><th></th><td>SE</td><td>2.74</td><td>2.38</td><td>1.61</td><td>3.09</td><td>2.62</td><td>2.35</td><td></td><td></td></tr><tr><th></th><td>LCL</td><td>15.43</td><td>16.65</td><td>9.22</td><td>12.67</td><td>14.53</td><td>16.26</td><td></td><td></td></tr><tr><th></th><td>UCL</td><td>26.26</td><td>26.04</td><td>15.56</td><td>24.92</td><td>24.88</td><td>25.50</td><td></td><td></td></tr><tr><th></th><td>Expected population size</td><td><i>279.20</i></td><td>283.20</td><td>162.85</td><td>241.43</td><td>258.40 277.03</td><td>250.35 ± 45.66</td></tr><tr><th></th><td>SE</td><td>38.04</td><td>32.35</td><td>21.82</td><td>41.95</td><td>35.64</td><td>31.92</td><td></td><td></td></tr><tr><th></th><td>LCL</td><td>214.03</td><td>226.54</td><td>125.37</td><td>172.18</td><td>197.45 221.20</td><td></td><td></td></tr><tr><th></th><td>UCL</td><td>364.20</td><td>354.03</td><td>211.52</td><td>338.52</td><td>338.18 346.96</td><td></td><td></td></tr><tr><th></th><td>Goodness of fit test <i>p</i> -value</td><td>>0.05</td><td><0.05</td><td>>0.05</td><td><0.05</td><td>>0.05</td><td>>0.05</td><td></td><td></td></tr></tbody></table><p>LCL = 95 % lower confidence limit; UCL = 95 % upper confidence limit; RHS, right hand side photos; LHS, left hand side photos; clipped 12,500 m = the selected habitat mask used for estimating hyaena densities – see Supplementary Material for full details.</p>
Data from: Divergence time estimation using fossils as terminal taxa and the origins of Lissamphibia
Were molecular data available for extinct taxa, questions regarding the origins of many groups could be settled in short order. As this is not the case, various strategies have been proposed to combine paleontological and neontological datasets. The use of fossil dates as node age calibrations for divergence time estimation from molecular phylogenies is commonplace. In addition, simulations suggest that the addition of morphological data from extinct taxa may improve phylogenetic estimation when combined with molecular data for extant species, and a few studies have combined morphological and molecular data to estimate combined-evidence phylogenies containing both fossil and extant taxa. However, to date, few if any studies have attempted to infer divergence time estimates using phylogenies containing both extinct and living taxa, sampled for both molecular and morphological data. Here, I infer both the phylogeny and time of origin for Lissamphibia using Bayesian methods, based on a dataset containing morphological data for extinct taxa, molecular data for a number of extant species, and molecular and morphological data for a subset of extant taxa. The results suggest that Lissamphibia is monophyletic, nested within Lepospondyli, and originated in the late Carboniferous at the earliest. This research illustrates potential pitfalls for the use of fossils as post-hoc age constraints on internal nodes, and highlights the importance of explicit phylogenetic analysis of extinct taxa. These results suggest that the application of fossils as minima or maxima on molecular phylogenies should be supplemented or supplanted by combined-evidence analyses whenever possible.
Figure 4 from: Del Latte L, Bortolin F, Rota-Stabelli O, Fusco G, Bonato L (2015) Molecular-based estimate of species number, phylogenetic relationships and divergence times for the genus Stenotaenia (Chilopoda, Geophilomorpha) in the Italian region. In: Tuf IH, Tajovský K (Eds) Proceedings of the 16th International Congress of Myriapodology, Olomouc, Czech Republic. ZooKeys 510: 31-47. https://doi.org/10.3897/zookeys.510.8808
Figure 4 - Dated phylogeny. Estimates of divergence time, calculated using 28S sequences and two priors (age of the root and substitution rate) in the package BEAST v1.7.2 (see text). 95% High Posterior Density intervals are represented by coloured bars for the most robust nodes, emphasized by a circle. Greek letters refer to the species tentatively recognised (see Fig. 1). The tree has the same topology of the concatenated ML tree of Fig. 3, but for the position of Tuoba sydneyensis and the relationships within the group formed by species β, ε, γ and the specimen from Iran. The specimen from Volpago (species δ) is absent because its 28S sequence was not obtained. Time scale is different in the two intervals 0–100 and 100–200 Ma.
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